- UCAS course code
- GG14
- UCAS institution code
- M20
Bachelor of Science (BSc)
BSc Computer Science and Mathematics
Duration: 3 years
Year of entry: 2025
UCAS course code: GG14 / Institution code: M20
- Key features:
Scholarships available
- Typical A-level offer: A*A*A including specific subjects
- Typical contextual A-level offer: AAA including specific subjects
- Refugee/care-experienced offer: AAB including specific subjects
- Typical International Baccalaureate offer: 38 points overall with 7,7,6 at HL, including specific requirements
Fees and funding
Fees
Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £36,000 per annum. For general information please see the undergraduate finance pages.
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
The University of Manchester is committed to attracting and supporting the very best students. We have a focus on nurturing talent and ability and we want to make sure that you have the opportunity to study here, regardless of your financial circumstances.
For information about scholarships and bursaries please visit our undergraduate student finance pages .
Course unit details:
Natural Language Understanding
| Unit code | COMP34812 |
|---|---|
| Credit rating | 10 |
| Unit level | Level 3 |
| Teaching period(s) | Semester 2 |
| Available as a free choice unit? | No |
Overview
Drawing from concepts covered in the prerequisite COMP34711: Natural Language Processing unit, this unit will enable students to look more deeply into how machines analyse and recognise meaning expressed in natural language. In this unit, students will gain hands-on experience in investigating solutions to a number of natural language understanding tasks. This will provide students with the know-how required to develop technologies for real-world applications enabling communication between humans and machines, which have become increasingly ubiquitous and indispensable.
Pre/co-requisites
| Unit title | Unit code | Requirement type | Description |
|---|---|---|---|
| Natural Language Processing | COMP34711 | Pre-Requisite | Compulsory |
Aims
The unit aims to:
- introduce students to the concepts and computational methods that enable machines to understand and interpret natural language
- explain the various tasks that underpin natural language understanding, and provide an overview of the state-of-the-art solutions to these tasks as well as their real-world applications
Learning outcomes
- To discuss the formulation of different natural language understanding tasks as sequence processing tasks e.g., sequence classification, sequence-to-sequence translation and sequence labelling.
- To differentiate between different types of parsing algorithms and apply them to natural language data to produce meaning representations.
- To compare different approaches to tasks such as named entity recognition and sentiment analysis.
- To relate natural language understanding tasks to applications such as question answering and conversational agents, among others.
- To develop a solution to a natural language understanding task with application to a real-world problem
Syllabus
- Introduction to NLU; Task formulations and applications
- Meaning representations: symbolic parsing and logical representations of sentences
- Vector-based representations (contextualised embeddings)
- Neural networks and neural language models
- Evaluation of models
- Sequence classification and textual entailment (and applications)
- Sequence labelling (and applications)
- Machine reading comprehension (and applications)
- Sequence-to-sequence translation (and applications)
- Limits and weaknesses of state-of-the-art approaches to NLU
Teaching and learning methods
Asynchronous lectures (weekly)
Synchronous workshops (weekly)
Labs (fortnightly)
Employability skills
- Analytical skills
- Problem solving
- Written communication
Assessment methods
| Method | Weight |
|---|---|
| Written exam | 70% |
| Practical skills assessment | 30% |
Feedback methods
Discussions and live coding during workshops (weekly)
Labs to support coursework (fortnightly)
Cohort-level feedback on exam
Study hours
| Scheduled activity hours | |
|---|---|
| Lectures | 20 |
| Practical classes & workshops | 10 |
| Independent study hours | |
|---|---|
| Independent study | 70 |
Teaching staff
| Staff member | Role |
|---|---|
| Riza Batista-Navarro | Unit coordinator |